[unreadable] Ovarian cancer is one of the leading causes of cancer death among females in the United States and Europe. Most of mortality is resulted from the late diagnosis of this tumor of cancer patients. Early diagnosis is the key to reduce the mortality rates of this tumor. It is urgent to develop markers for early diagnosis and for prognosis and for personalized medicine. Alteration of cytokine levels is associated with cancer progression, response to chemotherapy and metastatic status. Therefore, cytokines will provide new insight on cancer biology, identify new molecular targets for cancer treatment and discover new biomarkers for diagnosis and prognosis. However, since the limitation of technology, previous studies only measured single or few cytokines at once. This greatly limits our understanding of the roles of those factors in ovarian cancer and the potential application in clinical diagnosis and prediction of clinical outcome. Recently, we have developed the first cytokine antibody array systems to simultaneously detect the expression of multiple cytokines, chemokines, growth factors, angiogenic factors and proteases. Such systems feature high specificity and sensitivity. The ability to simultaneously detect multiple cytokines will greatly facilitate the biomarker discovery. Using this novel technology, we have examined the cytokine profiling from plasma samples of ovarian cancer patients and normal subjects. Our preliminary data suggest that the protein array systems we have developed may be used to identify new biomarkers for diagnosis and prognosis and profile for molecular classification. [unreadable] [unreadable] In this grant application, with the goal to identify biomarkers for diagnosis, prognosis and personalized medicine, we will apply protein array technology developed in the Principal Investigator's lab to profile the expression levels of approximate 200 different cytokines, chemokines, growth factors, angiogenic factors and proteases in plasma samples from normal subjects versus ovarian cancer subjects. The data will be analyzed using statistics and bioinformatics tool and correlated with pathological and clinical data. [unreadable] [unreadable] The successful outcome of this grant may impact on the development of new biomarkers for patient-care management. [unreadable] [unreadable]